Journals & Ranking

An academic journal or research journal is a periodical publication in which research articles relating to a particular academic discipline is published, according to Wikipedia. Currently, there are more than 25,000 peer-reviewed journals that are indexed in citation index databases such as Scopus and Web of Science. These indexes are ranked on the basis of various metrics such as CiteScore, H-index, etc. The metrics are calculated from yearly citation data of the journal. A lot of efforts are given to make a metric that reflects the journal's quality.

Journal Ranking Dataset

This is a comprehensive dataset on the academic journals coving their metadata information as well as citation, metrics, and ranking information. Detailed data on their subject area is also given in this dataset. The dataset is collected from the following indexing databases: - Scimago Journal Ranking - Scopus - Web of Science Master Journal List

The data is collected by scraping and then it was cleaned, details of which can be found in HERE.

Key Features

• Rank: Overall rank of journal (derived from sorted SJR index).
• Title: Name or title of journal.
• OA: Open Access or not.
• Country: Country of origin.
• SJR-index: A citation index calculated by Scimago.
• CiteScore: A citation index calculated by Scopus.
• H-index: Hirsh index, the largest number h such that at least h articles in that journal were cited at least h times each.
• Best Quartile: Top Q-index or quartile a journal has in any subject area.
• Best Categories: Subject areas with top quartile.
• Best Subject Area: Highest ranking subject area.
• Best Subject Rank: Rank of the highest ranking subject area.
• Total Docs.: Total number of documents of the journal.
• Total Docs. 3y: Total number of documents in the past 3 years.
• Total Refs.: Total number of references of the journal.
• Total Cites 3y: Total number of citations in the past 3 years.
• Citable Docs. 3y: Total number of citable documents in the past 3 years.
• Cites/Doc. 2y: Total number of citations divided by the total number of documents in the past 2 years.
• Refs./Doc.: Total number of references divided by the total number of documents.
• Publisher: Name of the publisher company of the journal.
• Core Collection: Web of Science core collection name.
• Coverage: Starting year of coverage.
• Active: Active or inactive.
• In-Press: Articles in press or not.
• ISO Language Code: Three-letter ISO 639 code for language.
• ASJC Codes: All Science Journal Classification codes for the journal.

Rest of the features provide further details on the journal's subject area or category: - Life Sciences: Top level subject area. - Social Sciences: Top level subject area. - Physical Sciences: Top level subject area. - Health Sciences: Top level subject area. - 1000 General: ASJC main category. - 1100 Agricultural and Biological Sciences: ASJC main category. - 1200 Arts and Humanities: ASJC main category. - 1300 Biochemistry, Genetics and Molecular Biology: ASJC main category. - 1400 Business, Management and Accounting: ASJC main category. - 1500 Chemical Engineering: ASJC main category. - 1600 Chemistry: ASJC main category. - 1700 Computer Science: ASJC main category. - 1800 Decision Sciences: ASJC main category. - 1900 Earth and Planetary Sciences: ASJC main category. - 2000 Economics, Econometrics and Finance: ASJC main category. - 2100 Energy: ASJC main category. - 2200 Engineering: ASJC main category. - 2300 Environmental Science: ASJC main category. - 2400 Immunology and Microbiology: ASJC main category. - 2500 Materials Science: ASJC main category. - 2600 Mathematics: ASJC main category. - 2700 Medicine: ASJC main category. - 2800 Neuroscience: ASJC main category. - 2900 Nursing: ASJC main category. - 3000 Pharmacology, Toxicology and Pharmaceutics: ASJC main category. - 3100 Physics and Astronomy: ASJC main category. - 3200 Psychology: ASJC main category. - 3300 Social Sciences: ASJC main category. - 3400 Veterinary: ASJC main category. - 3500 Dentistry: ASJC main category. - 3600 Health Professions: ASJC main category.

This single journal study investigated the predatory journal landscape in the information systems field, by analysing the International Journal of Applied Information Systems (IJAIS). The IJAIS was viewed from a bibliometric and citation perspective. The bibliometric study examined article productivity, authorship patterns and collaboration. An analysis was performed of 728 articles published in 11 volumes and 104 issues of IJAIS in the period 2012 to 2017. The citation study analysed citations to IJAIS papers in Scopus and Web of Science. A cited reference search was conducted in Scopus and Web of Science, and IJAIS papers cited in the list of references or works cited in Scopus or Web of Science papers were retrieved. Results of the bibliometric study revealed that the average number of articles per volume in IJAIS was 62. 1837 authors contributed to this journal. The journal displays a collaborative authorship pattern with 627 (86.13%) articles that were co-authored, compared to 101 (13.87%) that were single authored. The citation study showed that 751 Scopus papers cite IJAIS, of which 18 were review papers, compared to 153 Web of Science papers that cite IJAIS of which 9 were review papers. Of the 728 IJAIS articles published, 198 (27.2%) papers were cited in Scopus and Web of Science combined.

This description is part of the blog post "Systematic Literature Review of teaching Open Science" https://sozmethode.hypotheses.org/839

According to my opinion, we do not pay enough attention to teaching Open Science in higher education. Therefore, I designed a seminar to teach students the practices of Open Science by doing qualitative research.About this seminar, I wrote the article ”Teaching Open Science and qualitative methods“. For the article ”Teaching Open Science and qualitative methods“, I started to review the literature on ”Teaching Open Science“. The result of my literature review is that certain aspects of Open Science are used for teaching. However, Open Science with all its aspects (Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools) is not an issue in publications about teaching.

Based on this insight, I have started a systematic literature review. I realized quickly that I need help to analyse and interpret the articles and to evaluate my preliminary findings. Especially different disciplinary cultures of teaching different aspects of Open Science are challenging, as I myself, as a social scientist, do not have enough insight to be able to interpret the results correctly. Therefore, I would like to invite you to participate in this research project!

I am now looking for people who would like to join a collaborative process to further explore and write the systematic literature review on “Teaching Open Science“. Because I want to turn this project into a Massive Open Online Paper (MOOP). According to the 10 rules of Tennant et al (2019) on MOOPs, it is crucial to find a core group that is enthusiastic about the topic. Therefore, I am looking for people who are interested in creating the structure of the paper and writing the paper together with me. I am also looking for people who want to search for and review literature or evaluate the literature I have already found. Together with the interested persons I would then define, the rules for the project (cf. Tennant et al. 2019). So if you are interested to contribute to the further search for articles and / or to enhance the interpretation and writing of results, please get in touch. For everyone interested to contribute, the list of articles collected so far is freely accessible at Zotero: https://www.zotero.org/groups/2359061/teaching_open_science. The figure shown below provides a first overview of my ongoing work. I created the figure with the free software yEd and uploaded the file to zenodo, so everyone can download and work with it:

To make transparent what I have done so far, I will first introduce what a systematic literature review is. Secondly, I describe the decisions I made to start with the systematic literature review. Third, I present the preliminary results.

Systematic literature review – an Introduction

Systematic literature reviews “are a method of mapping out areas of uncertainty, and identifying where little or no relevant research has been done.” (Petticrew/Roberts 2008: 2). Fink defines the systematic literature review as a “systemic, explicit, and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners.” (Fink 2019: 6). The aim of a systematic literature reviews is to surpass the subjectivity of a researchers’ search for literature. However, there can never be an objective selection of articles. This is because the researcher has for example already made a preselection by deciding about search strings, for example “Teaching Open Science”. In this respect, transparency is the core criteria for a high-quality review.

In order to achieve high quality and transparency, Fink (2019: 6-7) proposes the following seven steps:

1. Selecting a research question.
2. Selecting the bibliographic database.
3. Choosing the search terms.
4. Applying practical screening criteria.
5. Applying methodological screening criteria.
6. Doing the review.
7. Synthesizing the results.

I have adapted these steps for the “Teaching Open Science” systematic literature review. In the following, I will present the decisions I have made.

Systematic literature review – decisions I made

1. Research question: I am interested in the following research questions: How is Open Science taught in higher education? Is Open Science taught in its full range with all aspects like Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools? Which aspects are taught? Are there disciplinary differences as to which aspects are taught and, if so, why are there such differences?
2. Databases: I started my search at the Directory of Open Science (DOAJ). “DOAJ is a community-curated online directory that indexes and provides access to high quality, open access, peer-reviewed journals.” (https://doaj.org/) Secondly, I used the Bielefeld Academic Search Engine (base). Base is operated by Bielefeld University Library and “one of the world’s most voluminous search engines especially for academic web resources” (base-search.net). Both platforms are non-commercial and focus on Open Access publications and thus differ from the commercial publication databases, such as Web of Science and Scopus. For this project, I deliberately decided against commercial providers and the restriction of search in indexed journals. Thus, because my explicit aim was to find articles that are open in the context of Open Science.
3. Search terms: To identify articles about teaching Open Science I used the following search strings: “teaching open science” OR teaching “open science” OR teach „open science“. The topic search looked for the search strings in title, abstract and keywords of articles. Since these are very narrow search terms, I decided to broaden the method. I searched in the reference lists of all articles that appear from this search for further relevant literature. Using Google Scholar I checked which other authors cited the articles in the sample. If the so checked articles met my methodological criteria, I included them in the sample and looked through the reference lists and citations at Google Scholar. This process has not yet been completed.
4. Practical screening criteria: I have included English and German articles in the sample, as I speak these languages (articles in other languages are very welcome, if there are people who can interpret them!). In the sample only journal articles, articles in edited volumes, working papers and conference papers from proceedings were included. I checked whether the journals were predatory journals – such articles were not included. I did not include blogposts, books or articles from newspapers. I only included articles that fulltexts are accessible via my institution (University of Kassel). As a result, recently published articles at Elsevier could not be included because of the special situation in Germany regarding the Project DEAL (https://www.projekt-deal.de/about-deal/). For articles that are not freely accessible, I have checked whether there is an accessible version in a repository or whether preprint is available. If this was not the case, the article was not included. I started the analysis in May 2019.
5. Methodological criteria: The method described above to check the reference lists has the problem of subjectivity. Therefore, I hope that other people will be interested in this project and evaluate my decisions. I have used the following criteria as the basis for my decisions: First, the articles must focus on teaching. For example, this means that articles must describe how a course was designed and carried out. Second, at least one aspect of Open Science has to be addressed. The aspects can be very diverse (FOSS, repositories, wiki, data management, etc.) but have to comply with the principles of openness. This means, for example, I included an article when it deals with the use of FOSS in class and addresses the aspects of openness of FOSS. I did not include articles when the authors describe the use of a particular free and open source software for teaching but did not address the principles of openness or re-use.
6. Doing the review: Due to the methodical approach of going through the reference lists, it is possible to create a map of how the articles relate to each other. This results in thematic clusters and connections between clusters. The starting point for the map were four articles (Cook et al. 2018; Marsden, Thompson, and Plonsky 2017; Petras et al. 2015; Toelch and Ostwald 2018) that I found using the databases and criteria described above. I used yEd to generate the network. „yEd is a powerful desktop application that can be used to quickly and effectively generate high-quality diagrams.” (https://www.yworks.com/products/yed) In the network, arrows show, which articles are cited in an article and which articles are cited by others as well. In addition, I made an initial rough classification of the content using colours. This classification is based on the contents mentioned in the articles’ title and abstract. This rough content classification requires a more exact, i.e., content-based subdivision and

The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

Malaria Journal Impact Factor 2024-2025 - ResearchHelpDesk - Aims and scope Malaria Journal is aimed at the scientific community interested in malaria in its broadest sense. It is the only journal that publishes exclusively articles on malaria and, as such, it aims to bring together knowledge from the different specialities involved in this very broad discipline, from the bench to the bedside and to the field. Open access All articles published by Malaria Journal are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. Further information about open access can be found here. As authors of articles published in Malaria Journal you are the copyright holders of your article and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate your article, according to the BMC license agreement. For those of you who are US government employees or are prevented from being copyright holders for similar reasons, BMC can accommodate non-standard copyright lines. Please contact us if further information is needed. Article-processing charges Open access publishing is not without costs. Malaria Journal therefore levies an article-processing charge of £1790.00/$2490.00/€2090.00 for each article accepted for publication, plus VAT or local taxes where applicable. If the corresponding author's institution participates in our open access membership program, some or all of the publication cost may be covered (more details available on the membership page). We routinely waive charges for authors from low-income countries. For other countries, article-processing charge waivers or discounts are granted on a case-by-case basis to authors with insufficient funds. Authors can request a waiver or discount during the submission process. For further details, see our article-processing charge page. Visit Springer Nature’s open access funding & support services for information about research funders and institutions that provide funding for APCs. Springer Nature offers agreements that enable institutions to cover open access publishing costs. Learn more about our open access agreements to check your eligibility and discover whether this journal is included. For more information on APCs please see our Journal Pricing FAQs Indexing services All articles published in Malaria Journal are included in: CABI CAS Citebase Current contents DOAJ Embase Global Health MEDLINE OAIster PubMed PubMed Central Science Citation Index Science Citation Index Expanded SCImago Scopus SOCOLAR Zetoc Zoological Record

The zip file enclosed contains README_Gamble_DevTox GLR_Sup Data_v1.docx, Gamble_DevTox GLR_Sup Fig_Submission_v1.docx, Gamble_DevTox GLR_Tables_Submission_v1.xlsx, Gamble_DevTox GLR_Sup Tables_Submission_v1.xlsx, ToxCast Pipeline plots for AEID 3093-3098. This dataset is associated with the following publication: Gamble, J., K. Hopperstad, and C. Deisenroth. The DevTox Germ Layer Reporter Platform: An Assay Adaptation of the Human Pluripotent Stem Cell Test. Toxics. MDPI, Basel, SWITZERLAND, 10(7): 392, (2022).

The Delphi method is an iterative, anonymous, group-based process for eliciting and aggregating opinion on a topic to explore the existence of consensus among experts. The year 2023 marks the 60th anniversary of the first peer-reviewed journal article on the Delphi method. Originally developed for operations research, this method is now applied extensively by researchers representing diverse scientific fields. We used a bibliometric analysis to describe general trends in the expansion of its use across disciplines over time. We conducted a systematic literature search for all English-language, peer-reviewed journal articles on the Delphi method through its first 60 years. We found 19,831 articles: 96.8% (n = 19,204) on the actual use of the Delphi method in an empirical study and 3.2% (n = 627) describing, examining, or providing some guidance on how to use the Delphi method. Almost half (49.9%) of all articles were published in the 2010s and an additional third (32.5%) in the first few years of the 2020s. Nearly two-thirds (65%, n = 12,883) of all published articles have appeared in medical journals, compared to 15% in science and technology (n = 3,053) or social science (n = 3,016) journals. We conclude that the expanded use of the Delphi method has been driven largely by the medical field, though social scientists and technologists continue to be at the forefront of methodological work on the Delphi method. Therefore, we call for greater transdisciplinary collaboration on methodological guidance and standards for the Delphi method.

The Delphi method is an iterative, anonymous, group-based process for eliciting and aggregating opinion on a topic to explore the existence of consensus among experts. The year 2023 marks the 60th anniversary of the first peer-reviewed journal article on the Delphi method. Originally developed for operations research, this method is now applied extensively by researchers representing diverse scientific fields. We used a bibliometric analysis to describe general trends in the expansion of its use across disciplines over time. We conducted a systematic literature search for all English-language, peer-reviewed journal articles on the Delphi method through its first 60 years. We found 19,831 articles: 96.8% (n = 19,204) on the actual use of the Delphi method in an empirical study and 3.2% (n = 627) describing, examining, or providing some guidance on how to use the Delphi method. Almost half (49.9%) of all articles were published in the 2010s and an additional third (32.5%) in the first few years of the 2020s. Nearly two-thirds (65%, n = 12,883) of all published articles have appeared in medical journals, compared to 15% in science and technology (n = 3,053) or social science (n = 3,016) journals. We conclude that the expanded use of the Delphi method has been driven largely by the medical field, though social scientists and technologists continue to be at the forefront of methodological work on the Delphi method. Therefore, we call for greater transdisciplinary collaboration on methodological guidance and standards for the Delphi method.

Journal of Chemistry Impact Factor 2024-2025 - ResearchHelpDesk - Journal of Chemistry is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles on all aspects of fundamental and applied chemistry. Journal of Chemistry is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative. It operates a fully open access publishing model which allows open global access to its published content. This model is supported through Article Processing Charges. Journal of Chemistry is included in many leading abstracting and indexing databases. For a complete list, click here. The most recent Impact Factor for Journal of Chemistry is 1.727 according to the 2018 Journal Citation Reports released by Clarivate Analytics in 2019. The journal’s most recent CiteScore is 1.32 according to the CiteScore 2018 metrics released by Scopus. Abstracting and Indexing Academic Search Alumni Edition Academic Search Complete AgBiotech Net AgBiotech News and Information AGRICOLA Agricultural Economics Database Agricultural Engineering Abstracts Agroforestry Abstracts Animal Breeding Abstracts Animal Science Database Biofuels Abstracts Botanical Pesticides CAB Abstracts Chemical Abstracts Service (CAS) CNKI Scholar Crop Physiology Abstracts Crop Science Database Directory of Open Access Journals (DOAJ) EBSCOhost Connection EBSCOhost Research Databases Elsevier BIOBASE - Current Awareness in Biological Sciences (CABS) EMBIOlogy Energy and Power Source Global Health Google Scholar J-Gate Portal Journal Citation Reports - Science Edition Open Access Journals Integrated Service System Project (GoOA) Primo Central Index Reaxys Science Citation Index Expanded Scopus Textile Technology Index The Summon Service WorldCat Discovery Services

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb)
It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

***Methodology***

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

***Test procedure***
Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study.
The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx)
The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

***Description of the data in this data set***

Protocol_HVD_SLR provides the structure of the protocol
Spreadsheets #1 provides the filled protocol for relevant studies.
Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories:
(1) descriptive information,
(2) approach- and research design- related information,
(3) quality-related information,
(4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet
2) Complete reference - the complete source information to refer to the study
3) Year of publication - the year in which the study was published
4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter}
5) DOI / Website- a link to the website where the study can be found
6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science
7) Availability in OA - availability of an article in the Open Access
8) Keywords - keywords of the paper as indicated by the authors
9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information
10) Objective / RQ - the research objective / aim, established research questions
11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.)
12) Contributions - the contributions of the study
13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach?
14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared?
15) Period under investigation - period (or moment) in which the study was conducted
16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)?
18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term?
20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output")
21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description)
22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles?
23) Data - what data do HVD cover?
24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt, .docx

***Licenses or restrictions***
CC-BY

For more info, see README.txt

Journal of Chemistry Acceptance Rate - ResearchHelpDesk - Journal of Chemistry is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles on all aspects of fundamental and applied chemistry. Journal of Chemistry is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative. It operates a fully open access publishing model which allows open global access to its published content. This model is supported through Article Processing Charges. Journal of Chemistry is included in many leading abstracting and indexing databases. For a complete list, click here. The most recent Impact Factor for Journal of Chemistry is 1.727 according to the 2018 Journal Citation Reports released by Clarivate Analytics in 2019. The journal’s most recent CiteScore is 1.32 according to the CiteScore 2018 metrics released by Scopus. Abstracting and Indexing Academic Search Alumni Edition Academic Search Complete AgBiotech Net AgBiotech News and Information AGRICOLA Agricultural Economics Database Agricultural Engineering Abstracts Agroforestry Abstracts Animal Breeding Abstracts Animal Science Database Biofuels Abstracts Botanical Pesticides CAB Abstracts Chemical Abstracts Service (CAS) CNKI Scholar Crop Physiology Abstracts Crop Science Database Directory of Open Access Journals (DOAJ) EBSCOhost Connection EBSCOhost Research Databases Elsevier BIOBASE - Current Awareness in Biological Sciences (CABS) EMBIOlogy Energy and Power Source Global Health Google Scholar J-Gate Portal Journal Citation Reports - Science Edition Open Access Journals Integrated Service System Project (GoOA) Primo Central Index Reaxys Science Citation Index Expanded Scopus Textile Technology Index The Summon Service WorldCat Discovery Services

Examples of XML, CSS, FO, and other necessary files to convert the JATS XML to various viewers and other XML types for scholarly publishing.

This is an xlsx file containing three sheets. Each sheet contains a listing of research article titles and DOIs on the topic of inequality. Each set of articles corresponds to a different journal that has published articles on inequality that have been tracked as mentioned online by Altmetric within the last year. V1 was revised as V2 to delete empty row in the second sheet and to correct title to clarify URLs are included. MethodologyThe source data was originally obtained with the Altmetric Explorer.The results of the searches for 'inequality' in the title mentioned in the last year were exported from the Altmetric Explorer as a spreadsheet and then the data was cleaned. This was done manually applying spreadsheet filters and adding and deleting columns, and using OpenRefine to deduplicate and standarise the data. Each output was then checked (i.e. each link was clicked on to visit the article pages) and a note was made to verfiy if the full version could be accessed without academic library credentials or not. Each table in each sheet contains the Altmetric score in timeframe (one year) in the first column and the outputs have been listed in that order (from the highest score to the lowest). Each article was checked one by one manually not using any institutional credentials or IP, and the access type of each article has been indicated in the last column. This file is shared here for educational, documental, archival and historiographic purposes.

This study estimates the effect of data sharing on the citations of academic articles, using journal policies as a natural experiment. We begin by examining 17 high-impact journals that have adopted the requirement that data from published articles be publicly posted. We match these 17 journals to 13 journals without policy changes and find that empirical articles published just before their change in editorial policy have citation rates with no statistically significant difference from those published shortly after the shift. We then ask whether this null result stems from poor compliance with data sharing policies, and use the data sharing policy changes as instrumental variables to examine more closely two leading journals in economics and political science with relatively strong enforcement of new data policies. We find that articles that make their data available receive 97 additional citations (estimate standard error of 34). We conclude that: a) authors who share data may be rewarded eventually with additional scholarly citations, and b) data-posting policies alone do not increase the impact of articles published in a journal unless those policies are enforced.

Public sector accounting systems comprise a structured collection of records which together document the financial transactions of the public agency.

From cash books and journals, sub-totals for expenditure and revenue were consolidated into subsidiary ledgers. Figures in subsidiary ledgers were used to compile totals of income and expenditure that were recorded in the general ledger. Categories of income and expenditure were then aggregated under account segments for use in financial statements.

The flow of information, however, is not always this straightforward.

Source Documents

Examples include receipt books, cheque butts, vouchers etc. Information is extracted from these documents and entered chronologically, in full or summary form, into cash books or journals. This process is called journalizing.

Cash Books

A cash book is a combination of a book of original entry (ie. a journal) and the ledger account for cash (often including the bank account). As a book of original entry it is used to record receipt and payment transactions in chronological order. Following a standard format, cash (and cheque) receipts are entered on the lefthand side of the book, and cash (and cheque) payments are recorded on the righthand side. These amounts are then 'posted' to the relevant ledger accounts which are identified either by ledger folio numbers or account numbers. As the cash book is also a replacement of the ledger account for cash, it is balanced at regular intervals.

Journals (Specific and General)

The prime function of a journal is to facilitate the 'posting' of credit and debit transactions into the appropriate ledger accounts. Like the cash book the journal is a book of original entry which records transactions in chronological order. Specific journals are often maintained to summarise information about similar types of transactions, including cash transactions, eg. cash receipts journal, wages and stores journal. General journals, on the other hand, provide a convenient record of other transactions, including adjustments to ledger accounts (to correct errors for example) and the sale or purchase of assets.

Journals may also be used to record the posting of amounts from one account to another (particularly common at the end of a financial year). The relevant accounts are identified either by the ledger folio number or an account number.

Ledgers (Subsidiary and General)

Ledgers comprise a record of changes (debit and credit transactions) concerning one or more accounts. The makeup (classification) of accounts is arbitrary and usually depends on the functions of the agency and the regulations governing its financial reporting requirements. Transactions are posted to the ledger accounts from the cash books and journals. The source of the posting is usually indicated by a combination of folio numbers and an abbreviation of the source record eg. 'C' or 'CB'=Cash Book, 'J'=Journal, 'PC'=Petty Cash Book etc.

Subsidiary ledgers are often maintained to facilitate a division of responsibilities within a large account, or to provide a separate record of a particular account.

A general ledger, however, comprises all accounts necessary for the compilation of the finance statements required by the agency. If subsidiary ledgers are used it is common for a general ledger to include a single account which represents the totals of the transactions of the accounts in each of the subsidiary ledgers. This device is called a 'control account'.

Finance Statements

Examples include Statements of Operations, Balance Sheets, Profit and Loss Statements.

Finance statements provide the final summary of the agency's financial situation at a particular point in time. They are usually compiled once a year and published with an annual report, although they may be compiled at more regular intervals. The types of statements and their format are generally determined by legislative requirements, and these in turn determine the nature of the accounts required to be maintained.

Context

The following shortlist of diamond open-access journals was compiled to increase awareness of alternative scholarly publication models among the six departments of the Faculty of Science at Utrecht University. The list is relevant to the six disciplines at the Faculty of Science: Biology, Chemistry, Mathematics, Information and Computing Sciences, Physics, and Pharmaceutical Sciences. For this purpose, a "diamond journal" is defined as a journal indexed in the Directory of Open Access Journals (DOAJ) that does not charge an article processing charge (APC).

Contents and Results

The Excel file titled “Diamond_journals_faculty_of_science_UU” contains the list of selected diamond journals based on the following criteria: they allow submissions in English, have a plagiarism screening policy, possess an electronic ISSN number, and accept submissions in Biology, Chemistry, Mathematics, Information and Computing Sciences, Physics, and Pharmaceutical Sciences. In this shortlist, 355 journals meet the criteria. Out of these 355 journals, only 29 have received a DOAJ seal, 150 journals are indexed in Scopus, and 94 journals are indexed in Web of Science.

A detailed description of the methods employed to obtain this shortlist can be found in the Word file titled "Methods_and_Results".

The raw CSV data has been included under the name "Raw_DOAJ_journal_metadata_2023_07_25".

Limitations

The compilers of this shortlist are aware that some current diamond journals could change their status to non-diamond by charging article processing fees at a later stage. Since the journal record is not always updated by the publishers, we strongly recommend the users double-check the latest open access status directly on the journal's homepage (journal URLs are provided in the Excel file). The same applies for Scopus and WOS indexations.

An alphabetical list of JATS elements. Visit https://dataone.org/datasets/sha256%3Ab48d09b1807b6e8459e363bcd9a9067a4b96aa4a56a2216382107cf5129270d4 for complete metadata about this dataset.

Australian and New Zealand journal of statistics Impact Factor 2024-2025 - ResearchHelpDesk - The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems. In addition, suitable review papers and articles of historical and general interest will be considered. The journal also publishes book reviews on a regular basis. Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Academic Search Elite (EBSCO Publishing) Academic Search Premier (EBSCO Publishing) CompuMath Citation Index (Clarivate Analytics) Current Index to Statistics (ASA/IMS) Journal Citation Reports/Science Edition (Clarivate Analytics) Mathematical Reviews/MathSciNet/Current Mathematical Publications (AMS) RePEc: Research Papers in Economics Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier) Statistical Theory & Method Abstracts (Zentralblatt MATH) ZBMATH (Zentralblatt MATH)

Stigmatizing language or non-person-centered language (PCL) has been shown to impact patients negatively, especially in the case of obesity. This has led many associations, such as the American Medical Association (AMA) and the International Committee of Medical Journal Editors (ICMJE) to enact guidelines prohibiting the use of stigmatizing language in medical research. In 2018, the AMA adopted PCL guidelines, including a specific obesity amendment that all researchers should adhere to. Our primary objective was to determine if PCL guidelines specific to obesity have been properly obeyed in the most interacted with sports medicine journals. We searched within PubMed for obesity-related articles between 2019 and 2022 published in the top ten most interacted sports medicine journals based on Google Metrics data. A predetermined list of stigmatizing and non-PCL terms/language was searched within each article.

This dataset contains free to read/open access status of scholarly journal articles from Stockholm University (Sweden) published between 2012-2017. The data published in xlsx and csv format. Only journal articles with a known DOI are included. The status of free/open access of the articles were checked manually and then compared to the Unpaywall/oaDOi database (https://unpaywall.org/data) in February 2018. The data was fetched with the help of the Unpaywall/oaDOI API: https://unpaywall.org/api/v2 Definitions of the columns in the data file: Article:DOI: DOI id of the ariclesu:DIVA PID: id of the article in the Stockholm University publication database (DiVA: http://su.diva-portal.org/)Journal: Name of the artcleYear: Publication year Manually checked data:Free to read at publisher homepage: 1 if the full-text of the article is free to read without registration at the publisher's homepageOA: 1 if the article has some kind of OA licenseLicense: Specification of the OA license (type of Creative Commons license, or "Other license"Gold OA journal: 1 if the journal is fully open accessPublisher: name of the publisher Data from oaDOIDOI found in oaDOI: 1 if the DOI is found in the oaDOI databaseoaDOI found something open: 1 if the oaDOI database found an open version availableFree to read at publisher homepage according to oaDOI: 1 if there is a free to read available version at the publisher's homepage according to oaDOIOA at publisher according to oaDOI best locationoaDOI best location: best free location according to oaDOIoaDOI data_standard: 1 or 2 according to oaDOI oaDOI license: license from the oaDOI databaseoaDOI license (standardized format): license type converted to the format of the column "License". License types other than Creative Commons are categorized as "Other license". Note: since many things were checked manually/half-automatically, some errors are inevitable. Furthermore all data was only accurate at the time of the check.